Services In Smart Spaces Research Articles

Designing Smart Space Services by Virtual Reality-Interactive Learning Model on College Entrepreneurship Education.

The purpose was to improve the limitations of traditional entrepreneurship education, realize the virtual interactive learning between college students and teachers, and stimulate students’ exploration of entrepreneurship. This work first discusses the working principle of Virtual Reality (VR) and builds an Interactive Learning Model (ILM) using VR. Then, the VR-ILM is used to design the Smart Space services. Harris Corner Detector (HCD) is used to detect the pixel grayscale change in the Smart Space image window. Further, the VR-ILM-based Smart Space is proposed according to the Smart Space design requirements and principles. Finally, the proposed VR-ILM-based Smart Space is applied to College Entrepreneurship Education (CEE). Its impact on the CEE market, employment in different industries, and students’ satisfaction with CEE are studied. The results show that the proposed VR-ILM-based Smart Space has increased the entrepreneurship teaching courses, entrepreneurship coaching activities, and entrepreneurship practice activities by 4, 6, and 24%, respectively. It has reduced entrepreneurship competitions and other forms of entrepreneurship education by 4 and 16%. The proposed VR-ILM-based Smart Space has dramatically improved the practical teaching of CEE. Meanwhile, real estate services have felt the most significant impact of the proposed VR-ILM-based Smart Space, with an employment increase of 43%. Lastly, students’ satisfaction with entrepreneurship education practice and teaching methods has increased by nearly 50%. The satisfaction with the internal environment has increased to 78%. The satisfaction with the curriculum system, teachers, and industry financing has increased from 30 to 45%, 24 to 36%, and 45 to 63%, respectively. The satisfaction with the teaching goal has increased to 62%. Thus, the proposed VR-ILM-based Smart Space has dramatically improved students’ satisfaction with CEE and has a different impact on the market, industry, and satisfaction with CE. The finding has a certain reference for the VR interactive model.

Human tracking and identification through a millimeter wave radar

The key to offering personalized services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workplaces. We propose a human tracking and identification system (mID11Parts of this work have been previously published in 2019 IEEE 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) Zhao et. al. (2019)[1].) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Using a low-cost, commercial, off-the-shelf radar, we first obtain sparse point clouds and form temporally associated trajectories. With the aid of a deep recurrent network, we identify individual users and show how to detect intruders. We evaluate and demonstrate our system, showing median position errors of 0.16 m, identification accuracy of 89% and intruder detection accuracy of 73% for 12 insiders. By increasing observation time from 2 s to 7 s, identification accuracy rises to 99%.

Unobtrusive and robust human identification using COTS RFID

Human identification is a prerequisite for many personalized services in smart spaces. This paper presents RFree-ID, the first unobtrusive RFID-based human identification system irrespective of walking cofactors (e.g. carrying a backpack or a briefcase). The key insight is that the RFID reader and tags can serve as a radio gate, and the walking-induced RF signal fluctuations from tags are capable of perceiving different walking patterns when people cross this gate. More importantly, spatially separated tags can provide abundant temporal and spatial information for amplifying discrepancies among people and minifying the influence of walking cofactors. Therefore, after collecting phase fluctuations received from RFID tags, RFree-ID identifies people by using a sequence of signal processing techniques and a well-designed matching algorithm. The system is implemented on COTS RFID devices, and extensive experimental evaluation under various conditions validates the high reliability and robustness of our system.

Recent Trends in User Authentication – A Survey

Recent advancements in technology have led to profusion of personal computing devices, such as smart phone, tablet, watch, glasses, and many more. This has contributed to the realization of a digital world where important daily tasks can be performed over the Internet from any place and at any time and using any device. At the same time, advances in pervasive computing technologies have brought to fruition the concept of smart spaces that target the automated provision of customized services to the inhabitants effortlessly. User authentication, i.e., a procedure to verify the identity of the user, is essential in the digital world so as to protect the user’s personal data stored online (e.g., online bank accounts) and on personal devices (e.g., smart phones) and to also enable customized services in smart spaces (e.g., adjusting room temperature and so on). Recently, traditional authentication mechanisms (e.g., passwords or fingerprints) have been repeatedly shown to be vulnerable to subversion. Researchers thus have proposed numerous new mechanisms to authenticate the users in the aforementioned scenarios. This paper presents an overview of these novel systems, so as to guide the future research efforts in these domains.

A Comparison of Head Pose and Deictic Pointing Interaction Methods for Smart Environments

Pointing is one of the more meaningful gestures in human expression. In this article, deictic and head pose pointing methods are evaluated as interaction means, with the objective of comparing their efficiency and delivered comfort, usability, and user experience to apply them to the deployment of services in smart spaces. Standard tools and questionnaires are applied in a user study with 20 individuals who have tried a Kinect V2-enabled pointing system to a) complete a multidirectional International Organization for Standardization (ISO) 9241-9 pointing task and b) test a service to command smart objects. The results show that both deictic and head pose interaction perform in a similar way, providing a throughput of 2.14 and 2.04 bits/s, respectively (comparable to joysticks or touchpads in equivalent ISO tasks). The system being quite robust to the user position in the Kinect coverage area, target size influences performance, but also learnability and the overall response when cursor visual cues are not available. From comfort, usability, and user experience responses, it can be said that deictic is perceived to perform better than head pose in terms of accuracy, efficiency, and organization, although head pose is preferred for its speed.

Nearest-neighbour query processing with non-spatial predicates for service allocation in smart space environment

The extensive capability of sensors let sensors autonomously collect various information on smart objects and store them to the spatial database through the wireless sensor networks. Based on spatial database, location-dependent information services (LDISs) can supply the resources according to the user locations. In LDISs, nearest-neighbour queries which return the closest object around the query location is recognised as the key component for searching the easily accessible services in smart spaces. However, existing works only consider the Euclidean distance. Thus, they have limitations to provide user-centric services that require the consideration for not only the distance but also the status of smart objects. Motivated by the issues of nearest-neighbour queries, this study proposes the new type of query called specified nearest-neighbour (SNN). SNN query considers the status and the locations of smart objects. For the SNN, the authors suggest a novel signature-based R-tree (SR-tree) index structure that handles non-spatial information of objects efficiently. Further, the authors propose an SNN query processing technique. Finally, they evaluate the performance of the proposed algorithm in various circumstances. Performance results indicate that SNN algorithm with SR-tree outperforms the existing works in terms of computational cost and disk input/output (I/O).

A breadboard architecture for pervasive context-aware services in smart spaces: middleware components and prototype applications

We present an architectural framework along with a set of middleware elements, facilitating the integration of perceptual components, sensors, actuators, and context-modeling scripts, comprising sophisticated ubiquitous computing applications in smart spaces. The architecture puts special emphasis on the integration of perceptual components contributed by a variety of technology providers, which has not been adequately addressed in legacy architectures. Moreover, the introduced architecture allows for intelligent discovery and management of resources. Along with the description of this breadboard architecture, we present its non-functional features and assess its performance. We also outline a rich set of practical prototype pervasive services that have been built, based on this architecture. These services emphasize on providing non-obtrusive human-centric assistance (e.g., memory aids, meeting recordings, pertinent information) in the scope of meetings, lectures and presentation, Experiences from building these services manifest the benefits of the introduced architecture.